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DirectX11 with Windows sdk--18 collision detection using the Directxcollision library

Objective In the DirectX SDK, the correlation function for collision detection is in xnacollision.h. Now, however, the previously implemented correlation functions have been transferred to the Windows SDK DirectXCollision.h and are in namespace DirectX. This consists mainly of four bounding boxes (bounding Volumes), and is implemented in the form of classes: Boundingsphere class-Surround ball (bounding Box) BoundingBox Class--axis aligned bou

Introduction to Intrusion Detection Systems

Introduction to Intrusion Detection Systems Chapter 1 concept of Intrusion Detection System As more and more companies transfer their core services to the Internet, network security is an unavoidable problem. Traditionally, companies generally adopt firewalls as the first line of defense for security. With the increasingly sophisticated knowledge of attackers and the increasingly complex and diverse att

Section 28th, the R-CNN algorithm of target detection algorithm

Girshick, Ross, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation." Proceedings of the IEEE Conference on Computer vision and pattern recognition. 2014.The full name of R-CNN is REGION-CNN, which can be said to be the first algorithm to successfully apply deep learning to target detection. The fast r-cnn, Faster r-cnn are all based on R-CNN.Most of the traditional targ

30th, Faster R-CNN algorithm of target detection algorithm

Ren, Shaoqing, et al. "Faster r-cnn:towards Real-time object detection with region proposal networks." Advances in neural information processing Systems. 2015.After Rcnn[1],fast Rcnn[2], this article is another masterpiece of the Ross Girshick team, the leader of the target detection community in 2015. The detection speed of simple network target is 17fps, the ac

Web detection of CENTOS7 's Zabbix

One, web monitoringWeb Scenarios (Web scene) is used to monitor web programs, can monitor the download speed of Web programs, return code and response time, but also support a set of continuous web actions as a whole to monitor.1, the principle of web monitoringWeb monitoring is the monitoring of the HTTP service, simulating the user to visit the site, to compare specific results, such as status code, return string, and other specific data for comparison and monitoring, so as to determine the av

A survey of the algorithm of moving target detection and tracking algorithms __

Image preprocessing Several typical noises in digital images are: The Gaussian noise originates from the noise of the electronic circuit and the sensor noise caused by low illumination or high temperature, and the noise of salt and pepper is similar to the particles of pepper and powder which are randomly distributed on the image, mainly by the image cutting or the error caused by the transform domain; In general, the introduction of the additive random noise, mean filter, median filter, Gaussia

Experience three network security online detection service _ Surfing

Computer popularization, broadband prevalence, now many friends online time is very long, so the security of the system has become "the most important." Fortunately, many security sites now provide online detection services, can be a good help us to detect the existence of their own computer vulnerabilities and security risks, so that timely and effective solutions to these problems.    first, the Internet Assistant Computer physical examination In

A target detection algorithm based on deep learning: YOLO

The target detection algorithm of the RCNN series previously studied was to extract the candidate regions, then use the classifier to identify the regions and position the candidate regions. The process of this kind of method is complex, there are some shortcomings such as slow speed and difficulty in training. The YOLO algorithm considers the detection problem as a regression problem, uses a single neural

Detailed description wireless intrusion detection system is required for wireless LAN

With the increase in hacker technology, wireless LAN (WLANs) is under more and more threats. Session hijacking and DoS attacks caused by misconfiguration of wireless base stations (WAPs) affect the security of Wireless LAN. Wireless networks are not only attacked based on the traditional wired network TCP/IP architecture, but may also be threatened by the security issues of the 802.11 standard issued by the Institute of Electrical and Electronics Engineers (IEEE. To better detect and defend agai

Wireless Intrusion Detection System

Now with the improvement of hacker technology, the wireless local area network (WLANS) is threatened more and more. The failure to configure a wireless base station (WAPS) causes session hijacking and denial of service attacks (Dos) to be like a plague that affects the security of wireless LANs in general. Wireless networks are vulnerable not only to the traditional wired network TCP/IP architecture but also to the security issues of the Institute of Electrical and Electronics Engineers (IEEE) r

Evaluation of IDS intrusion detection system

With the wide application of intrusion detection system, the requirement of testing and evaluating intrusion detection system is more and more urgent. Developers want to test and evaluate the deficiencies in the product, users want to test and evaluate to help themselves choose the right intrusion detection products. Based on the current research, this paper intr

Sobel Edge Detection algorithm

Sobel Edge Detection algorithmReprint Please specify source: http://blog.csdn.net/tianhai110The Bell operator (Sobel operator) is mainly used for edge detection, and technically, it is a discrete difference operator, which is used to calculate the approximate value of the grayscale of the image luminance function. Using this operator at any point in the image will produce a corresponding grayscale vector or

Disclosure of Windows server intrusion detection

Win2000 server Security Configuration, a carefully configured Win2000 server can protect against more than 90% intrusion and infiltration, but system security is a continuous process, with the advent of new vulnerabilities and server application changes, the security situation of the system is also changing At the same time, because of the contradictory unity of attack and defense, the magic long and the magic is also in constant conversion, so the system administrator can not guarantee that a s

Settings > Security > No onbody detection option under Smart lock

Settings > Security > No onbody detection option under Smart lock[DESCRIPTION] Settings > Security >smart Lock does not have the on-body detection option. [Solution] A description of this feature can be found on Google's official website: Https://support.google.com/nexus/answer/6093922?p=personal_unlockingrd=1 This feature is available on specific devices only. When you carry your device with you (for e

Hog characteristics of Image feature extraction from target detection

Hog Characteristics of image feature extraction from target detection Zouxy09@qq.com Http://blog.csdn.net/zouxy09 1. Hog Features: The directional gradient histogram (histogram of oriented Gradient, HOG) is a feature descriptor used for object detection in computer vision and image processing. It is characterized by calculating and statistic the gradient direction histogram of local region of image. Hog f

convolutional Neural Network (3): Target detection learning note [Wunda deep Learning]

.1.2.2 Training data (x, y), X for the picture, assuming 32*32*3, Y for the label, need to represent the classification and positioning of the position box, such as y= (PC, BX, by, BH, BW, C1, C2, C3), pc=1 that the picture target for pedestrians, cars, motorcycles, pc=0 means no target , as a background picture. The C1,C2,C3 is used to indicate which category the target is specifically classified. such as y= (1, 0.3, 0.6, 0.3, 0.4, 0, 1, 0) indicate the target for the car; y= (0,?,?,?,?,?,?,?)

Wunda deeplearning Automatic driving target detection

Wunda Automatic driving target detection data set: Automatic driving target detection autonomous Driving-car detection Welcome to your Week 3 programming assignment. You'll learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook is described in the YOLO Papers:redmon et al.,

Rapid Object Detection using a Boosted Cascade of simple Features partial translation

Rapid objectdetection using a Boosted Cascade of simple Features fast target detection using the easy feature cascade classifierNote: Some translations are not allowed in a red fontTranslation, Tony,[email protected]Summary:This paper introduces a vision application of machine learning in target detection, which can process images quickly and achieve a higher recognition rate. The success of this work is du

Perfect use of intrusion detection system in linux

Article Title: perfect solution for using the intrusion detection system in linux. Linux is a technology channel of the IT lab in China. Includes basic categories such as desktop applications, Linux system management, kernel research, embedded systems, and open source.    Introduction to intrusion detection systems As more and more companies transfer their core services to the Internet, network security is

Surf of pattern matching----feature point detection learning _2 (surf algorithm)

In the previous blog feature point detection learning _1 (SIFT algorithm), the classical SIFT algorithm is introduced briefly, the SIFT algorithm is stable, the detected feature points are also more, the biggest determination is the high computational complexity. There are many scholars to improve it, in which the more famous is the surf algorithm introduced in this paper, the Chinese meaning of surf is fast robust feature. This article is not specifi

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